Untangling proximity and accessibility effects of transit on property prices
Bibliographic record
Abstract
Most research interested in understanding the causal impacts of transit investments on property prices approach the topic through the lens of proximity. However, recent research highlights the networked nature of accessibility impacts. This presents challenges in causal research, particularly in mapping accessibility impacts, defining treatment and control groups, and estimating property price effects. Using the Evergreen Extension in Metro Vancouver, this research examines how proximity to new stations and changes in accessibility over time are capitalized into property prices, disentangling their distinct and combined effects. As commonly applied in the literature, the proximity-based measures identify treated properties based on their distance to the nearest station. For the accessibility-based approach, we measure changes in gravity-based scores over time to capture regional accessibility effects and treatment intensities along an ordered continuum, identifying areas experiencing accessibility gains, losses or stability. Results show that the spatial distribution of accessibility-based effects extend beyond conventionally-defined proximity catchments, suggesting an accessibility-based approach can better capture potential treatment effects associated with network spillovers. Next, fixed-effects models, using repeat sales, are then estimated to causally identify price premiums associated with both the proximity-based and accessibility-based measures. First, we find that, in line with urban economic theory, 1) proximity to new stations is positively valued, 2) increases in network access and regional connectivity to employment yield larger positive price effects, and 3) there is a combined premium placed on both proximity-based and accessibility-based benefits. Second, while proximity effects align with expectations, where treatment properties closer to the new stations command higher price premiums than those farther away, accessibility-based price effects do not. Specifically, higher treatment levels in the accessibility-based models do not consistently yield the highest premiums. This prompts an important perceptual question on whether homebuyers view proximity as a more intuitive signal of accessibility than broader regional network connectivity, which may be more intangible. We contribute knowledge through a dynamic approach that captures accessibility impacts for causal estimations, moving beyond the limitations of proximity-based measures in transit impact research. • Examines both proximity- and accessibility-based effects from a new transit system in Vancouver. • Highlights challenges in causal research, including mapping accessibility impacts, and defining control and treatment groups. • Accessibility impacts extend beyond conventional proximity catchments. • Both proximity and regional accessibility to employment, as well as their combined influence attract price premiums • Do homebuyers view proximity as a more intuitive signal of accessibility than regional network connectivity?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".